Alim, Usman RazaHornbeck, Haysn2018-10-022018-10-022018-09-21Hornbeck, H. (2018). Spatial Partitioning for Distributed Path-Tracing Workloads (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/33077http://hdl.handle.net/1880/108724The literature on path tracing has rarely explored distributing workload using distinct spatial partitions. This thesis corrects that by describing seven algorithms which use Voronoi cells to partition scene data. They were tested by simulating their performance with real-world data, and fitting the results to a model of how such partitions should behave. Analysis shows that image-centric partitioning outperforms other algorithms, with a few exceptions, and restricting Voronoi centroid movement leads to more efficient algorithms. The restricted algorithms also demonstrate excellent scaling properties. Potential refinements are discussed, such as voxelization and locality, but the tested algorithms are worth further exploration. The details of an implementation are outlined, as well.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.path tracingrenderingDistributed ComputingVoronoi diagramsout-of-core renderingComputer Sciencemaster thesis10.11575/PRISM/33077